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Uber's $1,500/month AI limit is a useful signal for AI tool pricing

simonwillison.net

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Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#221

Post by foobarian »

bijowo1676 wrote: post #171
dgellow wrote: post #164

Earlier quoted context omitted.

One aspect Paul Kedrosky mentioned recently is the concept of „duration mismatch“. The price per token goes down over time (either because the AI vendor reduces due to competition pressure, or because customers are now incentivized to use older cheaper models). But datacenters are financed through debt, with the assumption their revenue increases over time. Quoting him: „[AI vendors are] paying for a fixed cost with…

do GPU chips really depreciate physically? There are no moving parts, I dont think memory chips or GPU chips deteriorate naturally. I think its only accounting depreciation. I have been using my laptop for a decade, what is stopping datacenters from using the purchased GPU chips for a decade?

> There are no moving parts, I dont think memory chips or GPU chips deteriorate naturally

I believe they do, but I too would love to know more details because there are several ways this can happen. Electromigration, package failures, VRAM failures, dielectric breakdown... Hopefully there will be studies soon similar to that old Google paper on HDD failures!

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#222

Post by cmiles8 »

And $1500 a month is on the very high end of where most companies will land. When you run the numbers there isn’t a realistic path that connects the dots between likely market size and the claimed valuation of the AI companies. The math simply does not add up.

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#223

Post by CharlieDigital »

dgellow wrote: post #170
CharlieDigital wrote: post #4

$1500/mo is $18,000/seat/annum. Maybe Microsoft and Nvidia are on to something. 128 GB machines that can run local LLMs are a bargain even if priced $5-8k. Yes, tok/s is not quite there, but that's probably OK since the bottleneck really isn't the code; it's WTF did Uber build with all of that spend? How did it meaningfully impact their revenue in a positive direction?

You’re way better to run your own on premise models. Laptops are depreciating assets, do not benefit from economy of scale, have fixed specs, result in a fragmented fleet where you need to keep models up to date. Without talking about power consumption and cooling issues. I really don’t see why companies would go that direction

Even if the laptop costs $5k and you upgrade it every year with the latest hardware and run local models (assuming your workload can tolerate smaller models at slower tok/s), you win.

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#224

Post by thewebguyd »

pixelesque wrote: post #175
anthonypasq wrote: post #152

Earlier quoted context omitted.

perhaps the personal computer? Companies were spending 3-5k (10-15k inflation adjusted) on every employee for just hardware. everyone making comparisons to the dotcom bubble seems misguided. this is clearly computing 2.0 imo

Hardware's not generally a subscription, monthly cost though. You update it for them every 3/4 years (if they're lucky). It probably makes a bit more sense to compare it to existing software subscriptions like Office, or the old-school 'per-seat' licenses per user for software.

There's some software that can cost $1k or more per seat/month, but it's pretty rare. Big tier ERPs usually fall in the ~$600/seat/moth range, specialty engineering stuff can hit over $1k, Bloomberg terminal, etc. I wonder if what Uber's building with that $1.5k/month/employee is actually delivering the same value that something like an ERP would to the entire org...

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#225

Post by dghlsakjg »

eikenberry wrote: post #212
dghlsakjg wrote: post #190

Earlier quoted context omitted.

The majority of Deepseek providers on OpenRouter for v4 pro are in the US. Especially interesting is that they are in the same ballpark for pricing.

They are in the same ballpark for deepseek-v4-flash, but deepseek-v4-pro from deepseek is still around 1/2 of the alternatives.

I'm pretty sure that Deepseek said that pricing was promotional. Be curious to see if it lasts.

V3 pricing from them was right in line with what the commodity providers are charging.

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#226

Post by vb-8448 »

bijowo1676 wrote: post #171
dgellow wrote: post #164

Earlier quoted context omitted.

One aspect Paul Kedrosky mentioned recently is the concept of „duration mismatch“. The price per token goes down over time (either because the AI vendor reduces due to competition pressure, or because customers are now incentivized to use older cheaper models). But datacenters are financed through debt, with the assumption their revenue increases over time. Quoting him: „[AI vendors are] paying for a fixed cost with…

do GPU chips really depreciate physically? There are no moving parts, I dont think memory chips or GPU chips deteriorate naturally. I think its only accounting depreciation. I have been using my laptop for a decade, what is stopping datacenters from using the purchased GPU chips for a decade?

I used to work in datacenters, during spinning disk era we had technicians from vendors basically every couple of days to replace some broken part. When the massive switch to ssd happened instead of having them every couple of days it was 3 or 4 times per month.

Despite no moving parts things broke anyway and, even if it doesn't break, the vendor can make you change the technology just by playing with maintenance cost of the older one, limiting or removing spare parts from the market.

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#227

Post by threetonesun »

bijowo1676 wrote: post #171
dgellow wrote: post #164

Earlier quoted context omitted.

One aspect Paul Kedrosky mentioned recently is the concept of „duration mismatch“. The price per token goes down over time (either because the AI vendor reduces due to competition pressure, or because customers are now incentivized to use older cheaper models). But datacenters are financed through debt, with the assumption their revenue increases over time. Quoting him: „[AI vendors are] paying for a fixed cost with…

do GPU chips really depreciate physically? There are no moving parts, I dont think memory chips or GPU chips deteriorate naturally. I think its only accounting depreciation. I have been using my laptop for a decade, what is stopping datacenters from using the purchased GPU chips for a decade?

I assumed the issue was similar to crypto mining, where given finite amounts of space and power it makes sense to always be running the latest and most powerful GPUs instead of keeping older hardware running. There's definitely a secondary market for these GPUs as well.

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#228

Post by lavezzi »

_jab wrote: post #186
f311a wrote: post #37

How many more months do we need to wait, until big companies realize that flash models work just fine if you: 1) Don't ask LLMs for big changes 2) Review everything and point them in the right direction Large models still suck at big changes, they produce questionable architecture and you still have to review the code, if your project is serious enough. The codebase quickly become a mess, if you don't pay enough atte…

It's pretty simple; organizations are willing to tolerate paying $1500/month/engineer, which seems to be roughly inline with "normal" consumption for most full-time engineers. If that number grows significantly, then I bet companies will start exploring flash models more, as you propose.

They are willing to tolerate it now, which is quite a switch up from the free for all we had a few weeks ago, and if they aren’t able to tie in this new ~$1500p/m cap to demonstrable productivity and revenue increases then that will be kneecapped even faster

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#229

Post by CharlieDigital »

ftkftk wrote: post #92
jcgrillo wrote: post #20

Earlier quoted context omitted.

> WTF did Uber build with all of that spend? WTF did anyone build with all that spend? Despite all the feel-good anecdotes about how productive folks feel using ai coding tools there's a deafening silence when it comes to actual, demonstrated efficacy. How can we be this far entrenched in these workflows and still not know whether they actually do anything useful?

~70 FTE Engineering team. We are shipping more features, especially features that previously would not have survived the cut to make it on the roadmap. Even though we are shipping more, our total amount of escaped bugs has not increased, so our escape rate has actually lowered. On top of that we are able to triage and fix escaped bugs more quickly now. And then of course there has been an uptick in internal tooling t…

    > We are shipping more features
That's not really the important question; the important question: is it generating revenue.

If you increase your spend -> ship more features -> no correlated increase in revenue, that's just burning money.

If a team of 10 spends 1 extra headcount ($180k/year) and ships features with no corresponding growth in revenue, what does that mean?

There was probably a reason it was on the backlog (because it didn't really have value).

Re: Uber's $1,500/month AI limit is a useful signal for AI tool pricing

#230

Post by AgentMasterRace »

econ wrote: post #153
f311a wrote: post #37

How many more months do we need to wait, until big companies realize that flash models work just fine if you: 1) Don't ask LLMs for big changes 2) Review everything and point them in the right direction Large models still suck at big changes, they produce questionable architecture and you still have to review the code, if your project is serious enough. The codebase quickly become a mess, if you don't pay enough atte…

I wonder to what extent models should figure out which model to forward a query to. Or perhaps the big models could learn the difference between an easy and a hard question and charge accordingly? Perhaps, if it can measure complexity, even generate a quote? Small models are fine for small coding tasks but I don't see why big ones can't be broken down most of the time.

Many harnesses do this, I've recently dropped all my big subscriptions for using deepseek. Codewhale (formerly deepseek-tui) will use pro for large tasks and route smaller ones to flash. It's pretty good, but I just use pro and everything as the cost is quite low.

This one does not have routing, but reasonix is insane, absolutely insane for saving money. I've used 1.3billion tokens at the cost of 4$. (99-100% cache hit)

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